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Abstract
The article presents an approach to automated organization of textual data. The experiments have been performed on selected sub-set of Wikipedia. The Vector Space Model representation based on terms has been used to build groups of similar articles extracted from Kohonen Self-Organizing Maps with DBSCAN clustering. To warrant efficiency of the data processing, we performed linear dimensionality reduction of raw data using Principal Component Analysis. We introduce hierarchical organization of the categorized articles changing the granularity of SOM network. The categorization method has been used in implementation of the system that clusters results of keyword-based search in Polish Wikipedia.
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- Category:
- Articles
- Type:
- artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych
- Published in:
-
LECTURE NOTES IN COMPUTER SCIENCE
pages 140 - 149,
ISSN: 0302-9743 - Language:
- English
- Publication year:
- 2011
- Bibliographic description:
- Szymański J.: Self-Organizing Map representation for clustering Wikipedia search results// LECTURE NOTES IN ARTIFICIAL INTELLIGENCE. -., nr. No 6592 (2011), s.140-149
- Verified by:
- Gdańsk University of Technology
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